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IMM-UKF-PDA Tracker

Autoware package based on IMM-UKF-PDA tracker.

  • From a sourced terminal:

roslaunch lidar_tracker imm_ukf_pda_tracker.launch

  • From Runtime Manager:

Computing Tab -> Detection/ lidar_detector -> imm_ukf_pda_tracker

Reference

A. Arya Senna Abdul Rachman, 3D-LIDAR Multi Object Tracking for Autonomous Driving. 2017. paper

M. Schreire, Bayesian environment representation, prediction, and criticality assessment for driver assistance systems. 2017. paper

Requirements

  • eucledian_cluster node.
  • ray_ground_filter node.
  • /tf topic. Below video is from Suginami data which contais /tf topic: (autoware-20180205150908.bag). You can download it from ROSBAG STORE for free. Otherwise, you need to do localization with a map to produce /tf topic from velodyne to world.
  • wayarea info from vectormap if is possible.

Parameters

Launch file available parameters for imm_ukf_pda_tracker

Parameter Type Description
input topic String Input topic(type: autoware_msgs::CloudClusterArray). Default /cloud cluster.
output topic String Output topic(type: autoware_msgs::CloudClusterArray). Default /tracking_cluster_array.
pointcloud frame String Pointcloud frame. Default velodyne.
life time thres Int The minimum frames for targets to be visualized. Default 8.
gating thres Double The value of gate threshold for measurement validation. Default 9.22.
gate probability Double The probability that the gate contains the true measurement. Default 0.99.
detection probability Double The probability that a target is detected. Default 0.9.
distance thres Double The distance threshold for associating bounding box over frames. Default 100.
static velocity thres Double The velocity threshold for classifying static/dynamic. Default 0.5.
velocity_explosion thres Double The threshold for stopping kalman filter update. Default 1000.
use_sukf bool Use standard kalman filter. Default false.
is_debug bool Turning on debu mode. Publishing rosmarkers for debug. Default false.

Launch file available parameters for visualize_detected_objects

Parameter Type Description
input_topic String Input topic(type: autoware_msgs::CloudClusterArray). Default /tracking_cluster_array.
pointcloud frame String Pointcloud frame. Default velodyne.

Subscribed topics

Node: imm_ukf_pda_tracker

Topic Type Objective
/detection/lidar_objects autoware_msgs::DetectedObjectArray Segmented pointcloud from a clustering algorithm like eucledian cluster.
/tf tf Tracking objects in world coordinate.

Node: visualize_detected_objects

Topic Type Objective
/detected_objects autoware_msgs::DetectedObjectArray Objects with tracking info.

Published topics

Node: imm_ukf_pda_tracker

Topic Type Objective
/detected_objects autoware_msgs::DetectedObjectArray Added info like velocity, yaw ,yaw_rate and static/dynamic class to DetectedObject msg.
/bounding_boxes_tracked jsk_recognition_msgs::BoundingBoxArray Visualze bounsing box nicely in rviz by JSK bounding box. Label contains information about static/dynamic class

Node: visualize_detected_objects

Topic Type Objective
/detected_objects/velocity_arrow visualization_msgs::Marker Visualize velocity and yaw of the targets.
/detected_objects/target_id visualization_msgs::Marker Visualize targets' id.

Video

IMM UKF PDA lidar_tracker Autoware

Benchmark

Please notice that benchmark scripts are in another repository. You can tune parameters by using benchmark based on KITTI dataset. The repository is here.